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We report performance evaluation of our automatic feature discovery method on the publicly available Gisette dataset: a set of 29 features discovered by our method ranks 129 among all 411 current entries on the validation set. Our approach is a greedy forward selection algorithm guided by error clusters. The algorithm finds error clusters in the current feature space, then projects one tight cluster...
We report an automatic feature discovery method that achieves results comparable to a manually chosen, larger feature set on a document image content extraction problem: the location and segmentation of regions containing handwriting and machine-printed text in documents images. This approach is a greedy forward selection algorithm that iteratively constructs one linear feature at a time. The algorithm...
We propose a feature selection method that constructs each new feature by analysis of tight error clusters. This is a greedy, time-efficient forward selection algorithm that iteratively constructs one feature at a time, until a desired error rate is reached. The algorithm finds error clusters in the current feature space, then projects one tight cluster into the null space of the feature mapping,...
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